This invention relates generally to diagnostic imaging systems, and more particularly to systems and methods for determining a cortical thickness of an imaged brain using image data from a diagnostic imaging system.
Diagnostic imaging systems are used in many different applications. For example, magnetic resonance (MR) imaging systems may be used to image the structure and function of the human body. MR imaging of the brain has been used to evaluate different regions of the brain to determine pathological changes, which have been associated with different brain diseases such as Alzheimer's Disease, Parkinson's Disease, etc. These diseases often cannot be determined by confidence with standardized laboratory tests.
With respect to Alzheimer's Disease, for example, a change in the thickness of the cerebral cortex, which plays an important role in the memory and behavioral process of the brain has been used to identify the disease. The human cortex is a highly folded layer of neurons referred to as gray matter. Gray matter is a thin layer that covers the cerebrum. White matter supports the cerebral gray matter and connects various gray matter areas. Because of limited image resolution and partial volume effects when imaging the brain, the image intensities vary continuously from cerebral spinal fluid to gray matter and then to white matter. Accordingly, there is no clear boundary between white and gray matter in MR images, which is used to measure the thickness of the cortex.
Conventional methods used to measure the thickness of the cortex employ automatic segmentation processes to distinguish gray and white matters. These conventional methods are based on preset criteria to separate gray and white matter, which may result in less than satisfactory results. Using manual segmentation has similar problems, for example, image window leveling affects the identification of the gray and white matter boundary. These conventional segmentation methods are also complicated, resulting in a processor intensive and time consuming process. For example, to segment out the very complex thin layer of the cortex, active contour methods are known that rely mainly on gradient features for the segmentation of gray matter from an image and deformable models to segment the three-dimensional (3D) surface. However, not only are these methods computationally intense and time intensive, but the results of the gray matter segmentation are often not reliable or robust. In particular, the intensities of the brain gray matter overlap quite a lot with the intensities of the brain white matter. Also, the 3D structure of the brain is very complicated, especially on the gray and white matter interfaces. Additionally, limited spatial resolution and partial volume effects of the MR make it difficult to accurately find the pial surface and the interface between gray and white matters. Therefore, conventional methods are not robust or statistically well founded, resulting in problems with repeatability and comparison of results.